To ensure accuracy in its legal AI, LexisNexis unexpectedly hired a large number of lawyers, not just data scientists. These legal experts are crucial for reviewing AI output, identifying errors, and training the models, highlighting the essential role of human domain expertise in specialized AI.

Parvy's founders validated their idea by applying GPT-3 to 100 legal questions from Reddit. They sent the AI-generated answers to attorneys, who approved 86% without edits. This simple, real-world test was so effective it surprised even OpenAI's own legal team about their model's capabilities.

AI models reason well on Supreme Court cases by interpolating the vast public analysis written about them. For more obscure cases lacking this corpus of secondary commentary, the models' reasoning ability falls off dramatically, even if the primary case data is available.

The legal system, despite its structure, is fundamentally non-deterministic and influenced by human factors. Applying new, equally non-deterministic AI systems to this already unpredictable human process poses a deep philosophical challenge to the notion of law as a computable, deterministic process.

AI's impact on the legal world is twofold. On one hand, AI tools will generate more lawsuits by making it easier for firms to discover and assemble cases. On the other hand, AI will speed up the resolution of those cases by allowing parties to more quickly analyze evidence and assess the strengths and weaknesses of their positions, leading to earlier settlements.

The judicial theory of "originalism" seeks to interpret laws based on their meaning at the time of enactment. This creates demand for AI tools that can perform large-scale historical linguistic analysis ("corpus linguistics"), effectively outsourcing a component of legal reasoning to AI.

Unlike simple "Ctrl+F" searches, modern language models analyze and attribute semantic meaning to legal phrases. This allows platforms to track a single legal concept (like a "J.Crew blocker") even when it's phrased a thousand different ways across complex documents, enabling true market-wide quantification for the first time.

Within the last year, legal AI tools have evolved from unimpressive novelties to systems capable of performing tasks like due diligence—worth hundreds of thousands of dollars—in minutes. This dramatic capability leap signals that the legal industry's business model faces imminent disruption as clients demand the efficiency gains.

The legal profession's core functions—researching case law, drafting contracts, and reviewing documents—are based on a large, structured corpus of text. This makes them ideal use cases for Large Language Models, fueling a massive wave of investment into legal AI companies.